Enhancing Efficiency and Accuracy of Imbalanced Datasets Using Fuzzy Neural

In Data Mining the class Imbalance classification problem is considered to be one of the emergent challenges. This problem occurs when the number of examples that represents one of the classes of the dataset is much lower than the other classes. To tackle with imbalance problem, preprocessing the datasets applied with oversampling method (SMOTE) was previously proposed. Generalized instances are belonging to the family of NGE(abbreviate), which achieves storing objects in Euclidean n-space. The most representative mode used in NGE learning is: classical-BNGE and RISE, recent-INNER, rule induction-RIPPER and PART. In this paper, we propose a Fuzzy Neural Network approach, which is a combination of fuzzy logic and neural networks and called as Neuro Fuzzy System, which could improve the performance and accuracy of the existing system.(explain data set). The proposed approach is compared with NGE learning using SMOTE methods. explain validation/statistical method.